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<root>
<key>TrainRegression-libsvm</key>
<exec>otbcli_TrainRegression</exec>
<longname>TrainRegression (libsvm)</longname>
<group>Learning</group>
<description>Train a classifier from multiple images to perform regression.</description>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputImageList">ParameterMultipleInput</parameter_type>
<key>io.il</key>
<name>Input Image List</name>
<description>A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict.</description>
<datatype />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
<key>io.csv</key>
<name>Input CSV file</name>
<description>Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given</description>
<isFolder />
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
<key>io.imstat</key>
<name>Input XML image statistics file</name>
<description>Input XML file containing the mean and the standard deviation of the input images.</description>
<isFolder />
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
<key>io.out</key>
<name>Output regression model</name>
<description>Output file containing the model estimated (.txt format).</description>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>io.mse</key>
<name>Mean Square Error</name>
<description>Mean square error computed with the validation predictors</description>
<minValue />
<maxValue />
<default>0.0</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>sample.mt</key>
<name>Maximum training predictors</name>
<description>Maximum number of training predictors (default = 1000) (no limit = -1).</description>
<minValue />
<maxValue />
<default>1000</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>sample.mv</key>
<name>Maximum validation predictors</name>
<description>Maximum number of validation predictors (default = 1000) (no limit = -1).</description>
<minValue />
<maxValue />
<default>1000</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>sample.vtr</key>
<name>Training and validation sample ratio</name>
<description>Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5).</description>
<minValue />
<maxValue />
<default>0.5</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier</key>
<name>Classifier to use for the training</name>
<description>Choice of the classifier to use for the training.</description>
<options>
<choices>
<choice>libsvm</choice>
</choices>
</options>
<default>0</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.libsvm.k</key>
<name>SVM Kernel Type</name>
<description>SVM Kernel Type.</description>
<options>
<choices>
<choice>linear</choice>
<choice>rbf</choice>
<choice>poly</choice>
<choice>sigmoid</choice>
</choices>
</options>
<default>0</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.libsvm.m</key>
<name>SVM Model Type</name>
<description>Type of SVM formulation.</description>
<options>
<choices>
<choice>epssvr</choice>
<choice>nusvr</choice>
</choices>
</options>
<default>0</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.libsvm.c</key>
<name>Cost parameter C</name>
<description>SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins.</description>
<minValue />
<maxValue />
<default>1</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
<key>classifier.libsvm.opt</key>
<name>Parameters optimization</name>
<description>SVM parameters optimization flag.</description>
<default>True</default>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
<key>classifier.libsvm.prob</key>
<name>Probability estimation</name>
<description>Probability estimation flag.</description>
<default>True</default>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.libsvm.eps</key>
<name>Epsilon</name>
<description />
<minValue />
<maxValue />
<default>0.001</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.libsvm.nu</key>
<name>Nu</name>
<description />
<minValue />
<maxValue />
<default>0.5</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>rand</key>
<name>set user defined seed</name>
<description>Set specific seed. with integer value.</description>
<minValue />
<maxValue />
<default>0</default>
<optional>True</optional>
</parameter>
</root>
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